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  • Publication
    Accès libre
    Achieving Privacy in the Adversarial Multi-Armed Bandit
    (2017)
    Aristide C. Y. Tossou
    ;
    In this paper, we improve the previously best known regret bound to achieve ϵ-differential privacy in oblivious adversarial bandits from O(T2/3/ϵ) to O(T−−√lnT/ϵ). This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of information in T. However, we can improve this privacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach O(lnT−−−√)-DP, with a regret of O(T2/3) that holds against an adaptive adversary, an improvement from the best known of O(T3/4). This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.
  • Publication
    Accès libre
    Algorithms for Differentially Private Multi-Armed Bandits
    (2016)
    Aristide Tossou
    ;
    We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that there exist (ϵ,δ) differentially private variants of Upper Confidence Bound algorithms which have optimal regret, O(ϵ−1+logT). This is a significant improvement over previous results, which only achieve poly-log regret O(ϵ−2log2T), because of our use of a novel interval-based mechanism. We also substantially improve the bounds of previous family of algorithms which use a continual release mechanism. Experiments clearly validate our theoretical bounds.